Grid Asset Manager

ABSTRACT

An asset manager controls power distribution within an aggregated distributed energy resources system (“DERs system”) having a plurality of assets. The asset manager is configured to operate with a given asset. As such, the asset manager has 1) an interface to receive asset information relating to the given asset and to communicate with another asset manager in the DERs system, and 2) a function generator configured to produce a local cost function using data relating to the given asset only. The local cost function represents a portion of a system cost function for the DERs system. The asset manager also has 3) a controller configured to use the local cost function for the given asset to manage operation of the given asset in the DERs system. In addition, the controller also is configured to determine, using the local cost function, an operating point for the given asset.

PRIORITY

This patent application claims priority from provisional U.S. patentapplication No. 62/540,974, filed Aug. 3, 2017, entitled, “DISTRIBUTEDOPTIMIZATION FOR MICROGRIDS,” and naming Jorge Elizondo Martinez, AlbertTak Chun Chan, and Jose Jamil Dunia Dandah as in inventors, thedisclosure of which is incorporated herein, in its entirety, byreference.

FIELD OF THE INVENTION

Illustrative embodiments of the invention generally relate to powerdistribution networks and, more particularly, illustrative embodimentsof the invention relate to devices for managing power distributionacross a power network.

BACKGROUND OF THE INVENTION

The electric grid connects homes, buildings, and a wide variety ofdevices/systems to centralized power sources. This interconnectednesstypically involves centralized control and planning, which, undesirably,can cause grid vulnerabilities to rapidly cascade across the network. Tomitigate these risks, those in the art have formed “aggregateddistributed energy resources systems” (referred to herein for simplicityas “DERs systems”). By way of example, a “microgrid” is one suchimplementation of a DERs system. Specifically, among other qualities,microgrids often include controlled clusters of electric generationdevices and loads that provide a coordinated response to a utility need.A microgrid also can operate in a state in which it is connected to themain grid or disconnected from the main grid. These features, amongother things, improve DER efficiency, resiliency, and reliability.

The US Department of Energy formally defines a microgrid as a group ofinterconnected loads and distributed energy resources (“DERs”) withclearly defined electrical boundaries. When used together, this groupacts as a single controllable entity with respect to the main grid. Tothose ends, a microgrid often has distributed electric generators (e.g.,diesel generators and gas turbines, etc.), batteries for power storage,and renewable power resources, such as solar panels, hydroelectricstructure, and wind turbines.

SUMMARY OF VARIOUS EMBODIMENTS

In accordance with one embodiment of the invention, an asset manager isconfigured to control distribution of power within an aggregateddistributed energy system (“DERs system”) having a plurality of assets.To that end, the asset manager is configured to operate with a givenasset in the DERs system. As such, the asset manager has 1) an interfaceconfigured to receive asset information relating to the given asset andto communicate with at least one other asset manager (or other device,such as a central controller) in the DERs system, and 2) a functiongenerator operatively coupled with the interface. The function generatoris configured to produce a local cost function using data related to thegiven asset only (e.g., environmental temperature local to the givenasset, power requirements, etc.). The local cost function represents aportion of a system cost function for the overall DERs system. The assetmanager also has 3) a controller operatively coupled with the functiongenerator. The controller is configured to determine, using the localcost function, an operating point for the given asset, and use thedetermined operating point for the given asset to manage operation ofthe given asset in the DERs system.

The interface may be configured to receive one or more cost functionsfrom other asset managers. As such, the controller may forward controlsignals to the other asset managers to manage distribution of energy ofthe DERs system as a function of the local cost function and thereceived one or more cost functions.

The local cost function can be formed with a plurality of differentvariables. For example, the local cost function may include at least aportion relating to opportunity cost. To refine processes, theopportunity cost may include tunable parameters that the controller isconfigured to modify to improve revenue of the given asset. The localcost function also may include at least a portion relating to responselimitations of the given asset relative to a function of the givenasset. To that end, in response to receipt of commands to the givenasset, the controller may be configured to produce a given response withresponse data relating to the given asset. Accordingly, the controllermay be configured to measure the response data and calculate one or moreresponse limitations of the given asset using the measured responsedata.

Moreover, the local cost function may be inversely proportional to theasset efficiency at a given operating point and/or the given asset'spower rating. In some embodiments, the local cost function includesexpected future conditions relating to the given asset.

The controller further may be configured to receive operating data fromthe given asset, and then use the operating data to determine thelong-term effects on the given asset and/or the given asset's responsetime and/or efficiency. In that case, the function generator may use thelong-term effects on the given asset, and/or the given asset responsetime and/or the given asset efficiency to produce the local costfunction of the given asset.

In accordance with another embodiment of the invention, a method ofdistributing power from an aggregated distributed energy resourcessystem (“DERs system”) having a plurality of assets uses a plurality ofasset managers to manage the assets. Each asset includes a localdedicated asset manager separate from the other asset managers, and eachasset manager has an interface to receive asset information relating toits asset. For each asset, the method produces a local cost functionusing its local dedicated asset manager. Each local dedicated assetmanager produces its local cost function using data relating to itslocal asset only. The cost functions of the plurality of assets in theDERs system together represent a grid cost function for the overall DERssystem. The method determines, using the local cost function, anoperating point for the given asset, and uses the determined operatingpoint for the given asset to manage operation of the given asset in theDERs system.

Illustrative embodiments of the invention are implemented as a computerprogram product having a computer usable medium with computer readableprogram code thereon. The computer readable code may be read andutilized by a computer system in accordance with conventional processes.

BRIEF DESCRIPTION OF THE DRAWINGS

Those skilled in the art should more fully appreciate advantages ofvarious embodiments of the invention from the following “Description ofIllustrative Embodiments,” discussed with reference to the drawingssummarized immediately below.

FIG. 1 schematically shows a power grid that may be implemented inaccordance with illustrative embodiments of the invention.

FIG. 2 schematically shows an asset manager configured in accordancewith illustrative embodiments of the invention.

FIG. 3A-3C schematically shows the different types of use cases formicrogrid control: Grid connected, off-grid (Master-Slave), and off-grid(Droop).

FIGS. 4A-4C schematically show ways in which the asset can beconstructed in accordance with illustrative embodiments of theinvention.

FIGS. 5A-5D schematically show building blocks to determine thefundamental input variables for loss calculation.

FIG. 6 shows a process of calculating the range and variance of a set ofinput values and waits until they both exceed a threshold.

FIG. 7 graphically shows an example of using a non-uniform sequence offuture times that can range from very fast (e.g., sub-second and second)to very slow (e.g., hours and days), to leverage the weighted sum offuture costs in those time ranges, as the cost function to minimize.

FIG. 8 graphically shows an example of an overlap in ranges leading to ahysteresis region that can avoid instabilities in a virtual marketduring operation.

FIG. 9A shows a generalized process of managing a grid in accordancewith illustrative embodiments of the invention.

FIG. 9B shows a more specific process of managing a grid in accordancewith illustrative embodiments of the invention.

FIG. 10 schematically shows nested aggregated DERs systems.

FIG. 11 graphically shows an example of using real data to learn theturn-on, turn-off times of the assets, and leveraging those to definethe optimal turn-on and turn-off conditions.

FIG. 12 graphically shows an example of defining level threshold curvesand using those to change the output power of an asset within discretepower levels.

FIG. 13 graphically shows an example of using limited tunable parametersto adjust the cost function at each asset independently.

FIG. 14 graphically shows an example of accounting for the asset'sresponse limitations to determine the asset optimal response and usingreal data to tune the response limitations parameters over time.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

In illustrative embodiments, an aggregated distributed energy resourcessystem (“DERs system” as noted above), such as a microgrid, a group ofmicrogrids, and/or a larger grid, distributes intelligence between someor all of its assets to more efficiently manage power generation, use,and/or distribution. To that end, a DERs system configured in thismanner may operate using a system-level cost function that is managed atthe asset level. Specifically, each asset has an independent costfunction that it and/or an asset manager (discussed below) maintains.Among other ways, some embodiments may implement such a system with acentral controller in a manner that dynamically and more efficientlyupdates the system-level cost function. Accordingly, the day-to-dayoperation of the DERs system typically should be more efficient andresponsive than known prior art techniques, while at the same time beingless cumbersome to manage. Details of illustrative embodiments arediscussed below.

FIG. 1 schematically shows an exemplary DERs system implemented inaccordance with illustrative embodiments of the invention. The DERssystem includes an electrical power network that interconnects the loadsand DERs, including cables, transformers, switches, etc. Furthermore,the DERs system may include a grid connection. Among other ways, thisDERs system may be implemented as a microgrid that connects with alarger grid (“Utility” in FIG. 1) through a central controller 12/SCADAdevice 12; i.e., a supervisory control and data acquisition device. Forsimplicity, this description discusses various microgrid embodiments,although those skilled in the art should understand that variousembodiments apply to other grid structures beyond microgrids.Accordingly, discussion of a microgrid is by example only and thus, notintended to limit various DERs system embodiments.

Generically, the microgrid of FIG. 1 is a grid entity capable ofgenerating, storing, and/or distributing electrical energy and thus,also is identified by reference number 10. The microgrid 10 of FIG. 1may supply energy for a specific purpose, such as to a prescribedbusiness (e.g., a power-hungry data center), a neighborhood, or fordistribution to remote consumers via a larger power grid.

As known by those in the art and defined by the US Department of Energy,a microgrid may be a group of interconnected loads and distributedenergy resources within clearly defined electrical boundaries that actsas a single controllable entity with respect to the larger grid. In amicrogrid implementation of a DERs system, a microgrid can connect anddisconnect from the larger grid to enable it to operate in bothgrid-connected or island-mode.

Accordingly, the microgrid 10 of FIG. 1 has a plurality of assets 14connected by conventional interconnect techniques, such as with cablesand other peripheral equipment (e.g., transformers). As also known bythose in the art, an asset 14 can be a load or a distributed energyresource. Specifically, a device that transforms electricity intodifferent types of energy may be considered a load. Exemplary loadsoften found in microgrids may include motors, pumps, HVACs, andillumination systems. Conversely, storage (e.g., batteries, flywheels,etc.) and generation devices (e.g., solar panels, wind turbines, dieselgenerators, gas turbine generators, etc.) may be considered distributedenergy resources. FIG. 1 schematically shows several of these differenttypes of assets 14.

As noted above, however, the DERs system of FIG. 1 may be configured tohave many of the functions of a microgrid, but not meet the precisedefinition of the US Department of Energy. For example, the DERs systemof FIG. 1 may operate in a manner that does not necessarily operate asin island mode, while also having many corresponding functions to thoseof a microgrid. For example, the DERs system may include a feeder in adistribution network that has dozens or hundreds of assets 14.

In accordance with illustrative embodiments, each asset 14 in themicrogrid 10 of FIG. 1 has a dedicated asset manager 16 to manage andcontrol at least portions of its operation within the network. Assets 14having asset managers 16 thus may be referred to as “controllable assets14.” As such, the asset managers 16 effectively may be considered toform a distributed intelligent network that can be controlled and usedby the central controller 12.

The asset managers 16 of FIG. 1 are co-located with and connected toassets 14, and can perform one or more of the following functions:

1) control the asset's output, such as its real and reactive poweroutput, and/or output voltage and frequency;

2) measure qualities of the asset 14 and the system (e.g., at the pointwhere the asset 14 connects with the system), such as the asset'sterminal voltage and frequency, operating parameters, and othervariables related to the asset 14 itself and/or the environment; and

3) communicate with other assets 14 or devices through a variety ofknown methods.

In preferred embodiments, the asset managers 16 enable a plug-and-playsolution for simple, modular deployment. As such, the asset managers 16may automatically reconfigure operation as assets 14 are added, removed,or modified from the microgrid 10. Moreover, the asset managers 16 alsomay have self-learning intelligence using machine learning andartificial intelligence technology, enabling the microgrid 10 to attainand preferably maintain optimal, close to optimal, or otherwise enhancedperformance. When implemented with an open framework, third partysoftware developers can add specially tailored software to the assetmanager functionality to customize operation for specific customerneeds.

It should be noted that although FIG. 1 shows all assets 14 as having anasset manager 16, some embodiments deploy the asset managers 16 forfewer than all assets 14. Other embodiments deploy single asset managers16 or groups of asset managers 16 to be shared among two of more sets ofassets 14. Accordingly, discussion of each asset 14 having a dedicatedasset manager 16 is for convenience and not intended to limit variousembodiments. Furthermore, some asset managers 16 may be physicallylocated in close proximity to its asset(s) 14 (e.g., physically adjacentto the asset 14). Other embodiments, however, may couple an assetmanager 16 remotely from its asset. For example, some embodiments mayuse a cloud model and implement the asset manager 16 functionality on adevice remote from the asset 14 it manages. The asset 14 therefore maybe located in Massachusetts, while the asset manager 16 may be deployedin California or China.

Those skilled in the art may deploy the asset manager 16 in adistributed manner local to the asset 14, remote from the asset 14, orboth local/and remote to/from the asset. For example, the asset manager16 may be implemented using a plurality of different, spaced apartmodules around the asset 14 itself. As another example, the assetmanager 16 may be implemented using a local set of one or more module(s)and a remote set of one or more module(s). Accordingly, the form factorand location of the asset manager 16 as being a single unit in a singlehousing physically adjacent to its asset 14 is for illustrative purposesonly and not intended to limit various embodiments of the invention.

The overall microgrid 10 has a system cost function (discussed below)used to control its operation based on a variety of factors (alsodiscussed below). Specifically, microgrids are complex systems thatrequire “dispatch logic” (i.e., a way to control the amount of powereach asset 14 may consume or produce at any given time). Such dispatchlogic may be configured to achieve a variety of potentially overlappingand/or conflicting goals, which may include one or more of (a)minimizing operating and fuel costs, (b) reducing carbon emissions, and(c) prolonging equipment lifetime, etc.

Prior art technologies known to the inventors use one of two main waysin to produce this dispatch logic:

Rules-Based Expert System:

-   -   With this approach, system experts heuristically create the        dispatch logic. These rules might include, for example, to        charge batteries during the day when there is solar energy        available, to start diesel generators when the batteries are        low, or to export/import energy to a battery according to a        specific market price. Though directionally correct, this        approach undesirably often requires customization to each        specific system and can lead to underperforming systems,        especially because many edge cases are not properly managed.

Centralized optimization:

-   -   With this approach, a central controller executes an        optimization algorithm. To do so, the central controller 1)        collects information from the devices in the microgrid to create        appropriate models, 2) sets up a variety of constraints, 3)        solves the overall system optimization function, and then 4)        obtains the dispatch logic from it.

While this latter approach can result in higher performing microgridsthan for the prior noted rules based expert systems, it has severaldrawbacks. First, this approach still requires a degree of customization(e.g., adding or removing agents changes the optimization function andits constraints). Second, the communication network has real-worldlimitations on how much data can be transferred (and analyzed) in realtime. For example, battery assets 14 typically send numerous uniqueoutputs, including real and reactive power, state of charge,temperature, voltages, etc. Meanwhile, gas turbines with CHP (combinedheat and power) generally transmit their own set of outputs, real andreactive power, efficiency, water flow, water temperature, etc. Thequantity and diversity of output variables can dramatically slow downthe optimization algorithms, making them incapable of reaching optimizedsolutions rapid enough for microgrid operations.

These solutions therefore highlight technical difficulties encounteredin attempting to solve a difficult technical problem—efficientlymanaging assets 14 in the microgrid 10 to operate in a rapid, scalable,efficient, and effective manner. At a generic level, the inventorssolved these technical problems by pushing cost functions to the assetmanagers 16. Specifically, each asset manager 16, which has control anda virtual and/or hard local connect to its asset 14, produces,maintains, and executes a local, customized cost function for the asset14 it manages.

To those ends, each asset 14 includes a local cost function. In general,as known by those in the art, a cost function quantifies losses in asystem and enables an asset 14 to operate at a specified operatingpoint. To that end, a system cost function is a mathematical functionconstructed with variables from grid assets 14 (in some cases, thesystem as well) in such a way that obtains an operating point byminimizing or otherwise processing it. Preferably, this operating pointis a peak efficiency, optimal, or desired operating point for a givensystem. Indeed, in illustrative embodiments, each asset manager 16 onlyhas to manage the variables of the particular asset 14 to which it isconnected. However, by aggregating the asset managers 16 in themicrogrid 10, as well as their corresponding local cost functions, thesystem cost function can account for all assets 14.

In preferred embodiments, the cost function relates asset variablestogether to achieve an operating point in which multiple objectives areachieved at the same time. These objectives may be on an asset 14 byasset 14 basis, or on a grid-wide basis. Depending on the systemrequirements, the cost functions of some or all the assets 14 may beused to form a grid-level cost function. Among others, those objectivesmay include:

(1) Power Rating:

Assets 14 respond according to their power capacity (i.e., larger assets14 provide more power with everything else being equal). This ensureslarger assets take a larger part of the load

(2) Long Term Effects:

Each asset manager 16 uses real data to consider the long-term effectson its own asset of any action when deciding how to operate.

(3) Efficiency:

Asset losses are minimized by taking into account the asset'sefficiency.

(4) Opportunity Cost:

Assets 14 account for expected conditions in the future to adjust itspresent behavior by tuning some parameters specific to maximize a localprofit function.

(5) Response Limitations:

Each asset manager 16 considers its asset's own output responselimitations when deciding how to operate so that the resulting plannedoutput power is feasible.

Accordingly, in preferred embodiments, the local cost functions areformed with information relating to one or more of objectives 1-5 above.For example, some embodiments may include objectives 1-3, 2-5, 3-4, 1-2,and 4, 1 and 3-4, or other combination of 2 or more objectives.

Accordingly, in preferred embodiments, the local cost functions areformed with information relating to one or more of objectives 1-4 above.For example, some embodiments may include objectives 1-3, 2-4, 3-4, 1,-2, and 4, 1 and 3-4, or other combination of 2 or more objectives. Theoperating point that results from accounting for all of these objectivesis referred to herein as the “optimal” operating point. As suggestedabove, other embodiments may not tune the parameters and variables tothe optimal operating point and instead, account for fewer than all ofthese objectives.

FIG. 2 schematically shows one of the asset managers 16 of FIG. 1configured in accordance with illustrative embodiments of the invention.As shown, the asset manager 16 of FIG. 2 has a plurality of componentsthat together perform some of its functions. Each of these components isoperatively connected by any conventional interconnect mechanism. FIG. 2simply shows a bus communicating each the components. Those skilled inthe art should understand that this generalized representation can bemodified to include other conventional direct or indirect connections.Accordingly, discussion of a bus is not intended to limit variousembodiments.

Indeed, it should be noted that FIG. 2 only schematically shows each ofthese components. Those skilled in the art should understand that eachof these components can be implemented in a variety of conventionalmanners, such as by using hardware, software, or a combination ofhardware and software, across one or more other functional components.For example, the controller 18 (discussed below) may be implementedusing a plurality of microprocessors executing firmware. As anotherexample, the controller 18 may be implemented using one or moreapplication specific integrated circuits (i.e., “ASICs”) and relatedsoftware, or a combination of ASICs, discrete electronic components(e.g., transistors), and microprocessors. Accordingly, therepresentation of the controller 18 and other components in a single boxof FIG. 2 is for simplicity purposes only. In fact, in some embodiments,the controller 18 of FIG. 2 is distributed across a plurality ofdifferent machines—not necessarily within the same housing or chassis.

It should be reiterated that the representation of FIG. 2 is asignificantly simplified representation of an actual asset manager 16.Those skilled in the art should understand that such a device may havemany other physical and functional components, such as centralprocessing units, communication modules, protocol translators, sensors,meters, etc. Accordingly, this discussion is in no way intended tosuggest that FIG. 2 represents all of the elements of an asset manager16.

The asset manager 16 thus includes the noted controller 18 configuredto, among other things, use local cost functions to manage operation ofits asset 14, and determine an operating point. The asset manager 16also includes memory 24 for storing asset data, an interface 20 tocommunicate with the asset 14 and other devices, and a functiongenerator 22 configured to produce a local cost function. Although theinterface 20 may communicate with the asset 14 using a protocol that maybe proprietary to its assigned asset 14, it preferably also communicateswith the central controller 12 and/or other asset managers 16 using acommunication protocol common to the microgrid 10. Each of thesecomponents and other components cooperate to perform the variousdiscussed functions.

Accordingly, illustrative embodiments implement a decentralized dispatchapproach. For effective operation, the cost function is minimized (e.g.,using a Lagrange multiplier) and, by way of example, may be representedas follows:

$\begin{matrix}{{\min\limits_{P}{J( {P,x,\Theta} )}}{{s.t.\mspace{14mu} {\sum\limits_{i}P_{i}}} = P_{D}}} & (1)\end{matrix}$

-   -   where J is the cost function,    -   P is a vector of the output of all controllable assets 14,    -   P_(D) the “demanded power”,    -   x is all the assets 14 states relevant to the cost function, and    -   Θ (theta) are external parameters relevant to the cost function.

As noted, in some embodiment implementing a decentralized dispatchapproach, the “dual-decomposition” method may be used to allow thesystem cost function to be written as a combination of the costfunctions for individual assets 14. In some embodiments, theoptimization is framed as a “broadcast” and “gather” procedure, where a“master” device (e.g., the central controller 12 or asset manager 16 ofone of the assets 14) is only required to perform a simple calculation.The bulk of the optimization is performed by each asset manager 16 inthe DERs system 10 and/or the asset 14 itself.

The decentralized approach may be considered a “virtual market” in whicha signal generated in a coordinated DERs system acts as a “pricesignal”, that increases in value when there is more demand than supplyof energy, and decreases when there is more supply than demand, and itis used by the asset managers 16 to determine the asset response oftheir own assets 14. The asset response is the determination of the realand reactive power outputs of the asset obtained by minimizing a costfunction of a plurality of its variables with respect to power.Illustratively, at least one of the following may be used to make thevirtual market function efficient, accurate, and generic:

I. One or more techniques implement the market without detailedknowledge of loads and renewable generation,

II. One or more techniques extend the framework to other energy types,

III. One or more techniques automatically construct a cost function inthe assets 14,

IV. One or more techniques incorporate assets 14 with discontinuouspower output or consumption, and

V. One or more techniques extend the virtual market concept to multipleDERs systems.

Each of the above as implemented in various embodiments and is explainedfurther in the corresponding sections that follow.

I) Implement the market without detailed knowledge of loads andrenewable generation.

One drawback of many optimization techniques known to the inventors isthat they typically require knowledge of the power consumed by the loadsand generated by all sources at all times to determine the value of thedemanded power P_(D) in Eq. 1. This is often hard to achieve because itrequires many technical challenges, such as monitoring points, causingan increase in the cost and complexity of the system, as well as makingit more prone to failure. The inventors recognized, however, that evenwithout knowledge of the exact load and renewable generation:

1) when a microgrid 10 is connected to the grid, only one power flowmonitoring point is required to fully implement the virtual market and

2) when the microgrid 10 is off-grid, no additional monitoring pointsare required at all. The following analysis of each use case ispresented:

Grid-Connected Systems:

As shown in FIG. 3A, all assets calculate their optimal power output(P₀*), and the price signal is generated measuring the power sent to thegrid and compared to the desired power to be sent to the grid. If morepower is sent to the grid than desired, then there is excess energy andprice decreases. The opposite for when less power is sent to the gridthan desired.

In various embodiments the demanded power is calculated as follows:

$\begin{matrix}{P_{D} = {{\sum\limits_{i}P_{i}} + {\Delta \; P_{grid}}}} & (2)\end{matrix}$

where Pi is the output of a controllable asset 14 (which is known), andΔP_(grid) is the difference between the power flowing to the grid andthe desired power flowing to the grid. The amount of power that isdesired to flow to the grid (to achieve a particular service to theutility) is determined by the central controller 12 or a peer assetmanager 16. Illustrative embodiments only need to measure the powerflowing to/from the grid to run an optimization (i.e., one monitoringpoint only). By reviewing this equation, the inventors recognized thatinformation for renewable generation and for loads is not required tocalculate demanded power.

Off-Grid Systems, Master-Slave Control:

For an off-grid system, in various embodiments, the approach used tocalculate demanded power may be determined based on whether the systemis in master-slave mode or droop control mode.

In Master-Slave control architectures, as shown in FIG. 3B, one of thecontrollable assets 14 operates as a Master (i.e., it sets the voltageand frequency) and the rest of the assets 14 operate as Slaves (i.e.,they inject real and reactive power). The Master cannot set its outputpower, since it is determined by the system, and so there is an errorbetween what the Master desired output is and real output (ΔP_(M)). Thisdifference is used to calculate the price signal. The demanded power iscalculated as:

$\begin{matrix}{P_{D} = {{\sum\limits_{slaves}P_{i}} + {\Delta \; P_{M}}}} & (3)\end{matrix}$

It is the sum of the power injections by the slave devices (which areknown) plus ΔP_(M). Specifically, ΔP_(M)=P_(M)−P_(M)* is the differencebetween the power produced by the Master source (P_(M)) and the powerthat the Master source should produce (P_(M)*). Since the Master sourceis a controllable asset 14, the value of its output power is known. Andsince the asset 14 participates in the “virtual market” optimization(i.e., the Master source sends bids and receive prices just as any otherasset 14, even though it is not dispatchable), the amount of power itshould produce to operate in the most optimal point is known. Therefore,no additional measuring points are needed to implement the optimization.

It should be noted that the equations for grid-connected and off-gridsystems are the same if the grid itself is considered to be a Mastersource. The difference is that the power produced by the Master in theoff-grid case is automatically known, whereas the grid-connected caserequires a measurement of the grid's power flow.

Off-Grid Systems, Droop Control:

In droop-controlled microgrids, there is no concept of Master or Slavesources because all assets 14 simultaneously react to changes in systemloads and generation by varying individual output voltage and frequency.In such a system, all assets act like Masters, they all calculate theiroptimal output but cannot set it, so there is an error in all assets(ΔP₀). The aggregation of all errors is used to calculate the pricesignal. Because of this, the sum of these differences (ΔP_(i)) will bethe demanded power by the system.

$\begin{matrix}{P_{D} = {\sum\limits_{i}{\Delta \; P_{i}}}} & (4)\end{matrix}$

In some embodiments, the fact that the assets 14 are implementing adroop function is relied on to calculate the demanded power (based onthe network's droop coefficients and the associated changes in voltageand frequency). For example, if the assets 14 are implementing a P-fdroop, in some embodiments the demanded power is calculated as:

$\begin{matrix}{P_{D} = {( {f - f_{ref}} ){\sum\limits_{i}m_{p,i}}}} & (5)\end{matrix}$

where:

-   -   f is the measured frequency,    -   fref the nominal frequency, and    -   Mpi the Pf droop coefficient for each individual controllable        asset 14.

Thus, as in the previous two cases, no measurement of load or renewablegeneration is needed to implement this equation. In addition, althoughother droop implementations (such as power-voltage relationships) willlead to different equations, the result is the same.

II.) Include Additional Energy Types in the Optimization Framework

The description above relates to optimization around the assets' 14 realpower output P_(i). However, by analyzing systems in terms of analogies,the framework described above operates as well for other energy types,including reactive power, heat, hydrogen, diesel fuel, gas, etc. Thesame equations described above can be used by defining a “demandedpower” for virtually any energy type (e.g., reactive power or heat),calculating a price signal for it following demand and supply rules,sending the price to all asset managers and allowing them to calculatean operating point for the new energy type using a local cost function.

Cost function calculation for different energy types can also make useof analogies. FIGS. 4A-4C show three different but equivalent systems.All three tie to the power system through a specific device (e.g.,inverter, VFD, power supply, etc.), referred to as a “system interactivedevice” (“SID”). In each system, there are also one or more powerprocessing devices (e.g., DC/DC converter, motor, pump, electrolyzer,compressor, etc.), and finally one or more storage devices (e.g.,battery, pressure tanks, etc.).

In the connections between the SID, power processing devices, andstorage components, there is a pair of variables that transmit the powerthrough a medium (e.g., wires, pipes, shaft, etc.): (1) An acrossvariable that is measured from a point in the medium and a reference(examples shown in FIGS. 4A-4C are V_(dc), V_(ac), rotational speed ω,pressure p); and (2) a through variable that is measured flowing throughthe medium (examples shown in FIGS. 4A-4C are I_(dc) I_(ac), torque τ,mass flow). The efficiency associated with the SID, each powerprocessing devices, and storage components can be calculated from thepower flows at both ends of a power device, or with the input energy toa storage device. A further construction can be completed for a moredetail analysis of the losses associated with a device to analyze seriallosses (associated with the through variables) and parallel losses(associated with the across variables). An example of the former is thecopper loss on the wires connecting a battery to an inverter or thepressure drop in a pipe, while an example of the latter is theself-discharge of batteries or leakage in pressurized hydrogen tanks.FIGS. 5A-5D show examples of how the losses elements (serial R, parallelL) of the four asset types sources, loads, bidirectional elements andpower processing. In those figures, S represents a storage reservoir andP and ideal processing device (no losses). In addition, “x” is a throughvariable, and “y” an across variable.

The concept of price signal for energy types distinct from electricitycan also be used.

III.) Construct the Cost Function in the Assets 14

One technical optimization challenge involves determining how to createthe cost function for each asset 14. As discussed above, the costfunction is often a combination of pre-determined terms: 1) the powerrating capability of the asset, 2) the real efficiency of the asset,which can vary depending on factors such as state of charge,temperature, etc., 3) the long-term effects of a given operation on theasset (e.g., battery degradation due to charge/discharge cycles), 4) theasset's opportunity cost (i.e., the ability of an asset to change itsoperation in the present time to obtain more value in the future), and5) the response limitations of the asset. Illustrative embodimentsdetermine these cost function terms with machine learning techniques andother means. By measuring input and output variables at each asset 14over time, various embodiments can accurately calculate many differentcost drivers, such as the actual efficiencies of an asset 14 as afunction of multiple variables.

For example, consider a cost function used to estimate losses within abattery. It is possible to construct an appropriate relationship ofenergy loss with a number of variables, and then use that function inthe optimization framework. Losses, however, will likely depend onvarious dynamically changing properties, such as the amount of powerbeing processed, the temperature of the battery, the temperature of theinverter, the state of charge of the battery, the grid voltage, etc.This underlying complexity historically leads to heuristicsimplifications of the cost function, which undesirably can result ininaccurate estimates. The same holds true for the cost functions ofother types of assets 14, including those of diesel generators, gasturbines, hydrogen electrolyzers, thermal storage systems, etc.

To mitigate these technical problems, illustrative embodiments usemachine learning techniques to create and continually refine asset costfunctions. The discussed decentralized microgrid 10 is well suited forthis approach: every asset 14 can monitor its own variables at a higherrate, leading to higher accuracy and faster convergence. For example, aregularized least squares regression technique may be used. In someembodiments, quadratic relationships between each variable associatedwith asset 14 losses/efficiencies may be used to estimate the impact ofasset 14 variables on cost.

Two challenges may arise, however, from this approach.

(a) The amount of data needed to be stored and processed is substantialand consequently, possibly impractical, and

(b) The choice of variables from inputs in the learning algorithm mayprovide poor results if these variables do not accurately encompass keydrivers of the underlying cost functions. Each of these technicalproblems and corresponding technical solutions is discussed immediatelybelow.

(a) The amount of data needed to be stored and processed is impractical.

The use of machine learning can result in the accumulation of a largeamount of data. In addition, the data might be mostly redundant. Forexample, various states of an asset 14 might stay the same for some timeperiod (e.g., constant frequency set point), so significant amounts ofdata may not be worth storing. In various embodiments, one or more ofthe following techniques mitigate and/or resolve these technical issues:

i) Define a minimum change in at least one state for the input/outputpair to be stored for future processing,

ii) Use of purely online learning technique for cost calculation. Thisis useful because only the present relevant input/output data isrequired to refine the cost calculation, so there is no need to storelarge amounts of data. A disadvantage is that this likely would be lessaccurate than batch algorithms,

iii) Use of a combination of batch and online learning with a functiondetermining when enough information has been gathered to perform a newregression. This technique calculates the range and variance of a set ofinput values and waits until they both go above a threshold. FIG. 6shows a process of implementing a procedure for such a solution. Theprocess may be performed in whole or in part by the asset manager 16,its controller 18, and/or another device (e.g., the central controller12). It should be noted that this process is substantially simplifiedfrom a longer process that may be used to measure the object.Accordingly, the process can have many steps that those skilled in theart likely would use. In addition, some of the steps may be performed ina different order than that shown, or at the same time. Those skilled inthe art therefore can modify the process as appropriate.

The process of FIG. 6 begins at step 600 by storing a new input/outputpoint, and then determining (step 602) if the variance and range of thestored data is above a threshold. If not, the process loops back to thebeginning step 600. If above the threshold, however, then the processcontinues to step 604 by performing a batch regression technique,updating loss function coefficients (step 606), and deleting orexternally backing up stored data (step 608). The process then loopsback to the first step 600 to repeat the process. Accordingly, thisprocess is intended to update the loss function coefficient whilelimiting unnecessary data storage.

(b) The choice of variables for inputs into the learning algorithm willprovide poor results if these variables do not accurately encompass keydrivers of the underlying cost functions.

Selecting the input variables for the regression technique requiresknowledge of the asset 14 under consideration. In some embodiments, thefollowing approach can select such set of input variables for any typeof energy resource. First, energy resources are divided into thefollowing building blocks.

-   -   Sources: Their power flow is unidirectional from a reservoir        (internal or external to the system) into the system. This could        be the microgrid utility connection, the gas flow from the gas        utility, the diesel flow from a diesel tank, etc.    -   Loads: Their power flow is unidirectional from the system into a        reservoir (including its conversion into heat or work). Lighting        and HVAC systems are examples of loads.    -   Bi-directional storage: Assets 14 with bi-directional power flow        and thus, they can take power or return power into a reservoir.        Examples include electrical batteries and thermal storage        systems.    -   Power processing: Assets 14 that take one form of energy and        convert in a different form. Examples include inverters, heat        exchangers, diesel generators, etc.

Various embodiments optimize the microgrid 10 at least in part by firstassociating an asset 14 with a generic cost function and then improvingthe cost estimate over time. As an example, one might set the initialcost function for all assets 14 to have a constant efficiency withrespect to power, only to update the function appropriately based onactual data. The same can be done for other variables in the same way.FIG. 4A-4C show how the approach for efficiency calculation can beapplied to a system of multiple energy types, and FIGS. 5A-5D show apossible generic representation of where the losses are expected in thefour asset types discussed above.

In various embodiments, the system learns over time better ways todispatch the assets 14. There is no need for manual customization, andthis general framework provides a powerful starting point forcalculating an asset's loss or efficiency. Apart from these fundamentalvariables, it should be noted that illustrative embodiments also includeexternal parameters that affect losses (ambient temperature, humidity,etc.) in the machine learning algorithms. To illustrate this, FIG. 7graphically shows how efficiency can be calculated using real data tothen use it in the function generator 22 to construct the cost functionfor the asset 14.

In various embodiments, as shown in FIG. 8, the asset managers 16 cancombine variables measured at the present time from the asset 14 withvariables estimated for future times to calculate the cost function atfuture times. It is possible that the “price signal” at future times canalso be given by an external device, although that is not a requirementas it can also be estimated by each asset manager 16. The asset 14response is calculated by minimizing a weighted sum of the costfunctions at present and future times. The future times can be uniformor non-uniform and range from very fast (i.e., sub-second and seconds)to very slow (i.e., hours and days).

In various embodiments, the asset managers 16 can change the asset 14operation in the present time to obtain more value in the future. Thismay be achieved by accounting for future values, stored energy variablesand degradation variables in the cost function. The impact of thosevariables on the cost function can be adjusted within a range withtunable parameters. This capability gives each asset manager 16 someability to change its own asset's 14 cost function to try to maximizeits performance. The asset's performance measure is completed on eachasset independently and is given by the “revenue of the asset”, which isdefined as the integral over time of the “price signal” multiplied bythe optimal output power found by the minimization of the cost function.This technique can be applied in a discrete or continuous time andfosters “competition” between all the assets in the DERs system tomaximize their own revenue, where each asset changes its own parametersbased on its own predictions about the future.

The disclosed optimization technique advantageously can be applied tovarious embodiments of DERs systems, such as a system of individualmicrogrids 10 as well as individual assets 14. Consider the exampleshown in FIG. 10, where one or more microgrids 10 (or other system) canparticipate alongside one or more individual assets to form a nestedsystem (e.g., systems inside systems) under a utility feeder. Eachindividual microgrid 10 and asset could participate (and bid) into thislarger virtual market. The resulting dispatch command for the microgridsbecomes the “Demanded” power within a microgrid 10, which becomes aninput into the internal optimization for each individual asset 14.

In this nested optimization scheme, in some embodiments, a new demandedpower for the feeder results in a “price signal” that is send to everymicrogrid 10 and independent asset 14. Each microgrid 10 and asset 14can then adjust its output power based on their individual costfunction. The construction of a cost function of a microgrid 10 can bedetermined with either a rule-based approach or a market-based system;i.e., the individual microgrids 10 can use the same optimization pricesignal procedure to dispatch their internal assets 14.

Accordingly, illustrative embodiments may be used to build distributedvirtual markets for microgrid optimization. The optimization ofmicrogrid operations can be improved by performing any one or more ofthe following, as discussed above:

1. Implement the market without detailed knowledge of loads andrenewable generation.

2. Extend the framework to other energy types,

3. Construct a cost function in the assets 14,

4. Incorporate assets 14 with uncertain or discontinuous power output orconsumption.

5. Extend the virtual market concept to the optimization of multiplemicrogrids 10.

Accordingly, FIG. 9A shows a generalized process of managing a grid(e.g., a microgrid 10) in accordance with illustrative embodiments ofthe invention. In a manner similar to FIG. 6, this process may beperformed in whole or in part by one or more of the asset managers 16,and/or other device(s) (e.g., the central controller 12). It should benoted that this process is substantially simplified from a longerprocess, and details of various implementations are discussed above. Theprocess therefore can have many steps that those skilled in the artlikely would use. In addition, some of the steps may be performed in adifferent order than that shown, or at the same time. Those skilled inthe art therefore can modify the process as appropriate.

The process begins at step 900, in which each asset manager 16interrogates its assigned asset 14. To that end, the controller 18 ofeach asset manager 16 may simply receive, via its interface 20, realtime and non-real time operational data from its asset 14, andinformation related to its asset 14 (e.g., temperature local to theasset 14). In addition, the controller 18 may forward signals to theasset 14 to determine other information about the asset 14, such as itsreaction to certain stimuli, and information requiring requests foraccess.

For example, as noted above, the cost function of one or more of theassets 14 may include at least a portion relating to responselimitations of the asset 14 relative to a function of the asset 14.Among other things, such a response limitation may include the maximumamount of power the asset 14 may produce. Thus, the controller 18 maycommand its asset 14, via the interface 20, to produce a given responsewith response data from the given asset 14, and then measure theresponse data from the asset 14. The asset managers 16 may store andretrieve relevant information in its memory 24, which may include one orboth of long-term and short-term data storage.

Illustrative embodiments may interrogate using other techniques. As asecond example, a given asset 14 may have an asset efficiency at a givenoperating point, and that asset 14 may have a cost function that isinversely proportional to its efficiency at the given operating point.Thus, the controller 18 may provide commands to the given asset 14 toproduce a response with response data from the given asset 14 andmeasure the response data. The controller 18 may use that measuredresponse data to calculate efficiency as a function of multiplevariables. The function generator 22 then may use the calculatedefficiency to produce the local cost function of the given asset 14(e.g., during below discussed step 902).

As a third example, the controller 18 may receive, via the interface 20,operating data from a given asset 14, and use the operating data todetermine given asset response time. The desired result of the costfunction minimization is the optimal output powers at present and infuture times (P*); and since, assets 14 might not always reactimmediately to a command, usually taking some time to start (whilestaying in its current output power) and then ramping to the new value(ramp rate), the optimal output (P*) must be adjusted to the shape givenby the response limitations. In this method, the “response limitationsshape” must shift to the left continuously to account for the fact thatthe command was sent. In some cases, an asset might decide to avoidsending any other command until the “response limitation shape” has beenshifted completely out to the left. The “response limitations shape”might not be known a priori, but the asset manager 16 can learn it overtime. Illustrative embodiments may use two methods to account theresponse limitations: (1) Find the optimal response as if there were nolimitations and then force them afterwards, or (2) solve theoptimization of the cost function as a constrained problem.

As a fourth example, the controller 18 may receive, via the interface20, operating data from a given asset 14, and use the operating dataalong with the expected price signal in the future to determine theideal turn-on and turn-off conditions and times of the asset 14, asshown in FIG. 11 (discussed below). Assets can be on or off, and somemight take a significant amount of time to change their state, makingthe decision of when to turn-on and off an impactful one. Asset managers16 can define and use turn-on and turn-off threshold curves and comparethem with the expected price signal, to determine when a start or stopcommand should be sent to the asset. Consider the case when an asset isoff:

If the “turn-on threshold curve” intersects the “expected price signal”curve, an “on time” (t_(on)) when the asset should be operational can bedefined. The start signal must be sent if “t_(on)” is less than the“turn-on time” of the device. The exact same procedure can be done todetermine when to stop an asset. The turn-on and turn-off thresholdcurves should be different to give the on and off conditions somehysteresis and can be modified depending on the asset conditions (e.g.,state of charge, fuel level, etc.). As with the response limitations,the turn-on and turn-off times of an asset 14 might not be known apriori but can be learned by the corresponding asset manager 16 overtime.

As yet a fifth example, the controller 18 may receive, via the interface20, operating data from a given asset 14, and use the operating data toextend the concept of turn-on/turn-off conditions and apply thresholdcurves to assets that have discrete power levels, as shown in FIG. 12(discussed below). There must be a level on and level off thresholdcurve to provide a hysteresis to the response an avoid oscillations.

Thus, using the information from the memory 24 and/or controller 18 ofstep 900 (among other information), the function generator 22 generatesa local cost function for the given asset 14 as discussed above (step902). Moreover, each asset manager may determine, using the local costfunction, an operating point for the given asset, and then use thedetermined operating point for the given asset to manage operation ofthe given asset in the DERs system. Using the plurality of local costfunctions, step 904 then produces a system cost function. As alsodiscussed above, the central controller 12 may complete this step andcommunicate with the asset managers 16 via their interfaces 20.

Finally, at step 906, an asset manager 16 and/or the central controller12 may manage energy generation and/or distribution in the microgrid 10using the system cost function. As discussed above, managementpreferably is dynamically controlled based on changing conditions in themicrogrid 10 and assets 14, which can dynamically change the local costfunctions—consequently dynamically changing the system cost function.Accordingly, compared to centralized prior art management schemesdiscussed above, managing the microgrid 10 in this local and distributedmanner enables the local asset managers to more rapidly and efficientlygenerate their local cost functions, which can be more easily integratedinto the system cost function.

FIG. 9B shows a more specific process of managing a grid in accordancewith illustrative embodiments of the invention. In a manner similar toFIGS. 6 and 9A, this process may be performed in whole or in part by oneor more of the asset managers 16, and/or other device(s) (e.g., thecentral controller 12). It should be noted that this process issubstantially simplified from a longer process, and details of variousimplementations are discussed above. The process therefore can have manysteps that those skilled in the art likely would use. In addition, someof the steps may be performed in a different order than that shown, orat the same time. Those skilled in the art therefore can modify theprocess as appropriate.

The process begins at step 910, which defines grid-level/system-levelobjectives, and then reads grid-level/system level power flows (step912). Next, the process produces price signals (step 914) and then, atstep 916, shares price signals among all. The process then monitorsand/or interrogates the asset at step 918, and produces the costfunction at step 920. The process then calculates theoperation/operating point for each controllable asset at step 922, andconcludes by managing energy distribution at step 924.

FIG. 13 graphically shows an example of using limited tunable parametersto adjust the cost function at each asset independently. As such, someembodiments use limited tunable parameters to adjust the cost functionat each asset independently.

To that end, “opportunity cost” refers to the ability of an asset tochange its operation in the present time to obtain more value in thefuture. This may be achieved by accounting for future values, storedenergy variables, and degradation variables in the cost function. Theimpact of those variables on the cost function can be adjusted within arange with tunable parameters.

Accordingly, this concept gives each asset some ability to change itscost function in an effort to maximize performance. The performancemeasure preferably is completed at each asset independently, and isgiven by the “Revenue” of the asset. “Revenue” may be calculated as theintegral over time of the price signal multiplied by the optimal outputpower found by the minimization of the cost function. It can becompleted either in discrete or continuous time. This concept opens up a“competition” between assets attempting to maximize their own Revenue,with each changing its own parameters based on its own predictions aboutthe future.

FIG. 14 graphically shows an example of accounting for the asset'sresponse limitations to determine the asset optimal response and usingreal data to tune the response limitations parameters over time. Theresult of the cost function minimization is the optimal output powers atpresent and in the future times (P*). Assets typically do not reactimmediately to a command, but usually take some time to start (while itstays in its current output power) and then ramp to the new value (ramprate). The optimal output (P*) preferably is adjusted to the shape givenby the response limitations. Note that the “response limitations shape”has to shift to the left continuously to account for the fact that thecommand was sent. An asset might decide to avoid sending any othercommand until the “response limitation shape” has been shiftedcompletely out to the left. Also note that the “response limitationsshape” might not be known a priori, but the asset manager can learn itover time.

Various embodiments may use two ways to account for the responselimitations:

Option 1: Find the optimal response as if there were no limitations andthen force them afterwards, or

Option 2: Solve the optimization of the cost function as a constrainedproblem.

FIG. 11, noted above, graphically shows an example of using real data tolearn the turn-on, turn-off times of the assets, and leveraging those todefine the optimal turn-on and turn-off conditions. Specifically, assetscan be on or off, and some of them take a significant amount of time tochange its state, making the decision of when to turn-on and offimportant. For example, a gas turbine might take 3-4 minutes to be readyto export power. Illustrative embodiments may use turn-on and turn-offthreshold curves and compare them with the expected price signal todetermine when the start or stop signal should be sent to the asset.

As an example, consider the case when an asset is off:

If the “turn-on threshold curve” intersects the “expected price signal”curve, an “on time” (ton) or can be defined. That is when the assetshould be operational. The start signal will be sent if “ton” is lessthan the “turn-on time” of the device. The same procedure may becompleted to determine when to stop an asset. The turn-on and turn-offthreshold curves should be different to give the on and off conditionssome hysteresis. Moreover, the threshold curves can be modifieddepending on the asset conditions (for example, state of charge, fuellevel, etc.). As with the response limitations, the turn-on and turn-offtimes might not be known a priori, but can be learned by the assetmanager 16.

FIG. 12, noted above, graphically shows an example of defining levelthreshold curves and using those to change the output power of an assetwithin discrete power levels. Specifically, illustrative embodimentsextend the concept of turn-on/turn-off conditions by applying the sameidea of threshold curves to assets that have discrete power levels. Theconcept is similar as the turn-on/turn-off. Preferably, a level on andlevel off threshold curve provide a hysteresis to the response and avoidoscillations.

FIGS. 3A-3C, mentioned above, schematically show the different types ofuse cases for microgrid control: Grid connected, off-grid(Master-Slave), and off-grid (Droop). This concept illustrativelyapplies for actual microgrids only. The typical implementation mayinclude grid-connected, where all assets calculate their optimal poweroutput (P0*), and the price signal is generated measuring the power sentto the grid and compared to the desired power to be sent to the grid. Ifmore power is sent to the grid than desired, then there is excess energyand price decreases. The opposite for when less power is sent to thegrid than desired.

In Master-Slave, all assets 14 (including Master) calculate theiroptimal output. The Master cannot set its output power (this isdetermined by the system), and so there is an error between the Masterdesired output and real output (ΔPM). This difference is used tocalculate the price signal.

In droop, all assets 14 act like Masters. In addition, all assets 14calculate their optimal output but cannot set it, so there is an errorin all assets (ΔP0). The aggregation of all errors is used to calculatethe price signal.

In the context of distributed asset managers 16, the above approach maybe advantageous because of the way distributed asset managers 16preferably are sited in front of microgrid assets 14, or simply assignedto control specific microgrid assets 14, and are able to collect data,process data, and dispatch assets 14 in real time. Some embodiments, asnoted above, further may be applied to centralized optimizationapproaches.

Various embodiments of the invention may be implemented at least in partin any conventional computer programming language. For example, someembodiments may be implemented in a procedural programming language(e.g., “C”), or in an object-oriented programming language (e.g.,“C++”). Other embodiments of the invention may be implemented as apre-configured, stand-along hardware element and/or as preprogrammedhardware elements (e.g., application specific integrated circuits,FPGAs, and digital signal processors), or other related components.

In an alternative embodiment, the disclosed apparatus and methods (e.g.,see the various flow charts described above) may be implemented as acomputer program product for use with a computer system. Suchimplementation may include a series of computer instructions fixedeither on a tangible, non-transitory medium, such as a computer readablemedium (e.g., a diskette, CD-ROM, ROM, or fixed disk). The series ofcomputer instructions can embody all or part of the functionalitypreviously described herein with respect to the system.

Those skilled in the art should appreciate that such computerinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Furthermore, suchinstructions may be stored in any memory device, such as semiconductor,magnetic, optical or other memory devices, and may be transmitted usingany communications technology, such as optical, infrared, microwave, orother transmission technologies.

Among other ways, such a computer program product may be distributed asa removable medium with accompanying printed or electronic documentation(e.g., shrink wrapped software), preloaded with a computer system (e.g.,on system ROM or fixed disk), or distributed from a server or electronicbulletin board over the network (e.g., the Internet or World Wide Web).In fact, some embodiments may be implemented in a software-as-a-servicemodel (“SAAS”) or cloud computing model. Of course, some embodiments ofthe invention may be implemented as a combination of both software(e.g., a computer program product) and hardware. Still other embodimentsof the invention are implemented as entirely hardware, or entirelysoftware.

The embodiments of the invention described above are intended to bemerely exemplary; numerous variations and modifications will be apparentto those skilled in the art. Such variations and modifications areintended to be within the scope of the present invention as defined byany of the appended claims.

Various innovations are listed immediately below, and those innovationsmay be combined to include one or more of the specified innovations:

1. An asset manager configured to control distribution of power withinan aggregated distributed energy resources system (“DERs system”) havinga plurality of assets, the asset manager being configured to operatewith a given asset in the DERs system, the asset manager comprising:

an interface configured to receive asset information relating to thegiven asset and to communicate with at least one other asset manager ora central controller in the DERs system;

a function generator operatively coupled with the interface, thefunction generator configured to produce a local cost function usingdata relating to the given asset only, the local cost functionrepresenting a portion of a system cost function for the overall DERssystem; and

a controller operatively coupled with the function generator, thecontroller configured to determine, using the local cost function, anoperating point for the given asset,

the controller also being configured to use the determined operatingpoint for the given asset to manage operation of the given asset in theDERs system.

2. The asset manager of innovation 1 wherein the interface is configuredto receive one or more cost functions from other asset managers, thecontroller configured to forward control signals to the other assetmanagers to manage distribution of energy of the DERs system as afunction of the local cost function and the received one or more costfunctions.3. The asset manager of innovation 1 wherein the local cost functionincludes at least a portion relating to opportunity cost.4. The asset manager of innovation 3 wherein the opportunity costcomprises tunable parameters that the controller is configured to modifyto improve revenue of the given asset.5. The asset manager of innovation 1 wherein the local cost functionincludes at least a portion relating to response limitations of thegiven asset relative to a function of the given asset.6. The asset manager of innovation 1 further comprising:

the controller being configured, in response to receipt of commands tothe given asset, to produce a given response with response data relatingto the given asset, the controller being configured to measure theresponse data and calculate one or more response limitations of thegiven asset using the measured response data.

7. The asset manager of innovation 1 wherein the given asset has anasset efficiency at a given operating point, the local cost functionbeing inversely proportional to the asset efficiency at the givenoperating point.8. The asset manager of innovation 1 wherein the given asset has a powerrating, the local cost function being inversely proportional to thepower rating.9. The asset manager of innovation 1 wherein the local cost functionincludes expected future conditions at non-uniform time intervalsrelating to the given asset.10. The asset manager of innovation 1 wherein the controller isconfigured to receive operating data from the given asset, and then usethe operating data to determine given asset response time and/or givenasset efficiency,

the function generator using the given asset response time and/or thegiven asset efficiency to produce the local cost function of the givenasset.

11. A method of distributing power from an aggregated distributed energyresources system (“DERs system”) having a plurality of assets, themethod comprising:

using a plurality of asset managers to manage the assets, each assetincluding a local dedicated asset manager separate from the other assetmanagers or a central controller, each asset manager having an interfaceto receive asset information relating to its asset;

for each asset, producing a local cost function using its localdedicated asset manager, each local dedicated asset manager producingits local cost function using data relating to its local asset only, thecost functions of the plurality of assets in the DERs system togetherrepresenting a system cost function for the overall DERs system;

determining, using the local cost function, an operating point for thegiven asset,

using the determined operating point for the given asset to manageoperation of the given asset in the DERs system.

12. The method of innovation 11 wherein a central agent uses the costfunction for each of the plurality of assets to manage distribution ofenergy of the DERs system, the central agent being at least one of theasset managers.13. The method of innovation 11 wherein each cost function is customizedto each asset.14. The method of innovation 11 wherein the cost function of each assetincludes at least a portion relating to opportunity cost.15. The method of innovation 14 wherein the opportunity cost comprisestunable parameters that its asset manager can modify to improve profitof its asset.16. The method of innovation 11 wherein the cost function of each assetincludes at least a portion relating to response limitations of theasset relative to a function of the asset.17. The method of innovation 16 further comprising:

providing commands to a given asset, using its given asset manager, toproduce a given response with response data from the given asset; and

measuring the response data,

one or more response limitations of the given asset being calculated byits given asset manager using the measured response data.

18. The method of innovation 11 wherein the asset includes one or moreof a load, storage device, and an energy generation device.19. The method of innovation 11 wherein each asset has an assetefficiency at a given operating point, the cost function of each assetbeing inversely proportional to the asset efficiency at the givenoperating point.20. The method of innovation 19 further comprising:

providing commands to a given asset, using its given asset manager, toproduce a given response with response data from the given asset; and

measuring the response data,

measured response data by the asset manager being used to calculateefficiency as a function of multiple variables, the calculatedefficiency used to create the local cost function of the given asset.

21. The method of innovation 11 wherein each asset has a power rating,the cost function of each asset being inversely proportional to itspower rating.22. The method of innovation 11 wherein a given cost function of a givenasset includes expected future conditions relating to the given asset.23. The method of innovation 11 further comprising:

receiving operating data from a given asset; and

the asset manager of the given asset using the operating data todetermine given asset response time and/or given asset efficiency,

said producing a local cost function comprising using the given assetresponse time and/or the given asset efficiency to produce the localcost function of the given asset.

24. A computer program product for use on a computer system fordistributing power from an aggregated distributed energy resourcessystem (“DERs system”) having a plurality of assets, the computerprogram product comprising a tangible, non-transient computer usablemedium having computer readable program code thereon, the computerreadable program code comprising:

program code for communicating with a plurality of asset managers tomanage the assets, each asset including a local dedicated asset managerseparate from the other asset managers, each asset manager having aninterface;

program code for producing, for each asset, a local cost function usingits local dedicated asset manager, each local dedicated asset managerproducing its local cost function using data relating to its local assetonly, the cost functions of the plurality of assets in the DERs systemtogether representing a system grid cost function for the overall DERssystem;

program code for determining, using the local cost function, anoperating point for the given asset; and

program code for using the determined operating point for the givenasset to manage operation of the given asset in the DERs system.

25. The computer program product of innovation 24 further comprisingprogram code for to control a central agent to use the cost function foreach of the plurality of assets to manage distribution of energy of theDERs system, the central agent being at least one of the asset managers.26. The computer program product of innovation 24 wherein the costfunction of each asset includes at least a portion relating toopportunity cost.27. The computer program product of innovation 26 wherein theopportunity cost comprises tunable parameters that its asset manager canmodify to improve profit of its asset.28. The computer program product of innovation 24 wherein the costfunction of each asset includes at least a portion relating to responselimitations of the asset relative to a function of the asset.29. The computer program product of innovation 24 further comprising:

program code for providing commands to a given asset, using its givenasset manager, to produce a given response with response data from thegiven asset; and

program code for measuring the response data,

one or more response limitations of the given asset being calculated byits given asset manager using the measured response data.

30. The computer program product of innovation 24 wherein each asset hasan asset efficiency at a given operating point, the cost function ofeach asset being inversely proportional to the asset efficiency at thegiven operating point.31. The computer program product of innovation 30 further comprising:

program code for providing commands to a given asset, using its givenasset manager, to produce a given response with response data from thegiven asset; and

program code for measuring the response data,

program code for controlling the asset manager to use measured responsedata to calculate efficiency as a function of multiple variables, thecalculated efficiency used to create the local cost function of thegiven asset.

32. The computer program product of innovation 24 wherein each asset hasa power rating, the cost function of each asset being inverselyproportional to its power rating.33. The computer program product of innovation 24 wherein a given costfunction of a given asset includes expected future conditions relatingto the given asset.34. The computer program product of innovation 24 further comprising:

program code for receiving operating data from a given asset; and

program code for using the operating data to determine given assetresponse time and/or given asset efficiency,

said program code for producing comprising program code for using thegiven asset response time and/or the given asset efficiency to producethe local cost function of the given asset.

What is claimed is:
 1. An asset manager configured to controldistribution of power within an aggregated distributed energy resourcessystem (“DERs system”) having a plurality of assets, the asset managerbeing configured to operate with a given asset in the DERs system, theasset manager comprising: an interface configured to receive assetinformation relating to the given asset and to communicate with at leastone other asset manager or a central controller in the DERs system; afunction generator operatively coupled with the interface, the functiongenerator configured to produce a local cost function using datarelating to the given asset only, the local cost function representing aportion of a system cost function for the overall DERs system; and acontroller operatively coupled with the function generator, thecontroller configured to determine, using the local cost function, anoperating point for the given asset, the controller also beingconfigured to use the determined operating point for the given asset tomanage operation of the given asset in the DERs system.
 2. The assetmanager of claim 1 wherein the interface is configured to receive one ormore cost functions from other asset managers, the controller configuredto forward control signals to the other asset managers to managedistribution of energy of the DERs system as a function of the localcost function and the received one or more cost functions.
 3. The assetmanager of claim 1 wherein the local cost function includes at least aportion relating to opportunity cost.
 4. The asset manager of claim 3wherein the opportunity cost comprises tunable parameters that thecontroller is configured to modify to improve revenue of the givenasset.
 5. The asset manager of claim 1 wherein the local cost functionincludes at least a portion relating to response limitations of thegiven asset relative to a function of the given asset.
 6. The assetmanager of claim 1 further comprising: the controller being configured,in response to receipt of commands to the given asset, to produce agiven response with response data relating to the given asset, thecontroller being configured to measure the response data and calculateone or more response limitations of the given asset using the measuredresponse data.
 7. The asset manager of claim 1 wherein the given assethas an asset efficiency at a given operating point, the local costfunction being inversely proportional to the asset efficiency at thegiven operating point.
 8. The asset manager of claim 1 wherein the givenasset has a power rating, the local cost function being inverselyproportional to the power rating.
 9. The asset manager of claim 1wherein the local cost function includes expected future conditions atnon-uniform time intervals relating to the given asset.
 10. The assetmanager of claim 1 wherein the controller is configured to receiveoperating data from the given asset, and then use the operating data todetermine given asset response time and/or given asset efficiency, thefunction generator using the given asset response time and/or the givenasset efficiency to produce the local cost function of the given asset.11. A method of distributing power from an aggregated distributed energyresources system (“DERs system”) having a plurality of assets, themethod comprising: using a plurality of asset managers to manage theassets, each asset including a local dedicated asset manager separatefrom the other asset managers or a central controller, each assetmanager having an interface to receive asset information relating to itsasset; for each asset, producing a local cost function using its localdedicated asset manager, each local dedicated asset manager producingits local cost function using data relating to its local asset only, thecost functions of the plurality of assets in the DERs system togetherrepresenting a system cost function for the overall DERs system;determining, using the local cost function, an operating point for thegiven asset, using the determined operating point for the given asset tomanage operation of the given asset in the DERs system.
 12. The methodof claim 11 wherein a central agent uses the cost function for each ofthe plurality of assets to manage distribution of energy of the DERssystem, the central agent being at least one of the asset managers. 13.The method of claim 11 wherein each asset has an asset efficiency at agiven operating point, the cost function of each asset being inverselyproportional to the asset efficiency at the given operating point. 14.The method of claim 11 wherein each asset has an asset efficiency at agiven operating point, the cost function of each asset being inverselyproportional to the asset efficiency at the given operating point,further comprising: providing commands to a given asset, using its givenasset manager, to produce a given response with response data from thegiven asset; and measuring the response data, is measured response databy the asset manager being used to calculate efficiency as a function ofmultiple variables, the calculated efficiency used to create the localcost function of the given asset.
 15. The method of claim 11 whereineach asset has a power rating, the cost function of each asset beinginversely proportional to its power rating.
 16. The method of claim 11wherein a given cost function of a given asset includes expected futureconditions relating to the given asset.
 17. The method of claim 11further comprising: receiving operating data from a given asset; and theasset manager of the given asset using the operating data to determinegiven asset response time and/or given asset efficiency, said producinga local cost function comprising using the given asset response timeand/or the given asset efficiency to produce the local cost function ofthe given asset.
 18. A computer program product for use on a computersystem for distributing power from an aggregated distributed energyresources system (“DERs system”) having a plurality of assets, thecomputer program product comprising a tangible, non-transient computerusable medium having computer readable program code thereon, thecomputer readable program code comprising: program code forcommunicating with a plurality of asset managers to manage the assets,each asset including a local dedicated asset manager separate from theother asset managers, each asset manager having an interface; programcode for producing, for each asset, a local cost function using itslocal dedicated asset manager, each local dedicated asset managerproducing its local cost function using data relating to its local assetonly, the cost functions of the plurality of assets in the DERs systemtogether representing a system grid cost function for the overall DERssystem; program code for determining, using the local cost function, anoperating point for the given asset; and program code for using thedetermined operating point for the given asset to manage operation ofthe given asset in the DERs system.
 19. The computer program product ofclaim 18 further comprising program code for to control a central agentto use the cost function for each of the plurality of assets to managedistribution of energy of the DERs system, the central agent being atleast one of the asset managers.
 20. The computer program product ofclaim 18 further comprising: program code for receiving operating datafrom a given asset; and program code for using the operating data todetermine given asset response time and/or given asset efficiency, saidprogram code for producing comprising program code for using the givenasset response time and/or the given asset efficiency to produce thelocal cost function of the given asset.